Tuesday, October 13, 2015

Kona power meter usage trends: 2009 to 2015

Update for 2015 based on the Lava Magazine bike count data. Previous posts links showing trend data up to 2013 and 2014 are here:


Without further ado, here are the numbers for 2009 through to to 2015 are (click on images to see larger versions):

In brief, 2015 continued the long term trend of an increase in use of power meters by Kona IM athletes, with a tick under half of all bikes now fitted with a power meter.

The two longest established brands, SRM and Powertap, have further fallen away in absolute numbers as well as total share dropping with Powertap suffering the biggest drop in usage, and while Quarq is still the most used meter, its absolute usage has reached a plateau and it is no longer as dominant a power meter brand for Kona IM athletes as it has been in the past few years. It will be interesting to see how Powertap fares in the years ahead with the introduction of their new pedal and chainring based meters.

The use of power meters is more evenly distributed across the various brands than in previous years, with no brand dominating share of usage on Kona IM athlete's bikes.

Newer power meter brands have increased their presence significantly, in particular Garmin Vector and especially Stages being the big movers.

Power2Max maintained their 2014 share of the power meter pie, while newer offerings from Rotor and Pioneer make up the smaller slices.

Thanks to Prof. Hendrik Speck of Hochschule Kaiserslautern University of Applied Sciences for picking up a couple of very small errors in the Polar power meter numbers I had listed for 2009 and 2013. I have updated the table and chart above. I also left the linked posts from previous year's summaries uncorrected so that a record of the small error remains.

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Monday, August 24, 2015

When your ride buddy becomes a real drag

A question that comes up from time to time when chatting about aerodynamics stuff is how much impact does another rider in close proximity have on your aerodynamics, or more correctly stated, does having another rider in close proximity change the power required for you to maintain your speed?

We are all familiar with the reduction in power required when riding behind another rider. This "drafting" benefit is substantial and anyone with a power meter can see the big reduction in power when they move from riding directly into the wind to riding behind another rider. Even if you don't have a power meter the difference is certainly large enough to notice the reduction in effort required.

But what about when your buddy is drafting behind you or rides beside you? Does this impact the power needed to maintain the same speed?

The short answer is: yes, both of them do.
But in what way and by how much?

The question as to whether a rider in front gains benefit from having a rider behind them has been researched before, and the consensus is that yes, they gain a small benefit. There is good reason for this slightly counter intuitive result and it's to do with the "bow wave" of air from the rider behind causing a change in the turbulent air flow behind the lead rider and reducing, by a small amount, the depth of the low pressure zone that exists behind the front rider.

This slight reduction in the fore to aft air pressure differential of the lead rider provides a small but measurable gain. This can be expressed as a reduction in apparent CdA, but since a rider's CdA doesn't really change if their position and equipment hasn't, then in reality it's a change in the forces acting on the rider, and as a result, the power demand at the same speed is slightly reduced when compared with having no rider in close proximity (or alternatively, a rider can travel slightly faster for the same power when they have a rider immediately behind them).

In 2010  Andy Coggan examined data from a 2007 track session ridden by his wife, in which she did efforts on the track both with and without having a rider drafting behind her. In Andy's assessment of the data he remarked "having a rider drafting closely behind them apparently lowered their CdA by 3.2%, i.e., from 0.198 to 0.192 m^2.".

The reduction in energy demand will be of a very similar amount to the reduction in apparent CdA. Assuming ~350W, a reduction from a CdA of 0.198 to 0.192 is equivalent to a reduction in power demand at the same speed of ~10W, or 2.8%. In this case the other rider was riding in pursuit set up, and were themselves very "aero" (an elite track pursuit rider).

So that's one example.

This phenomenon has also been reported in the published scientific literature, examples include:

Racing cyclist power requirements in the 4000-m individual and team pursuits, Medicine and Science in Sports and Exercise, v31, no.11, pp 1677-1685, 1999. J.P. Broker, C.R. Kyle and E.R. Burke.

where amongst their data they report that the lead rider requires 2-3% less power while riding on the front of a 4-man team than if riding solo at the same speed.

Another more recent study examined this using both computational fluid dynamics (CFD) simulations along with wind tunnel validation as described in this paper:
CFD simulations of the aerodynamic drag of two drafting cyclists, Computers & Fluids Volume 71, 30 January 2013, Pages 435–445,. Bert Blocken, Thijs Defraeyeb, Erwin Koninckxc, Jan Carmelietd, Peter Hespelf

In this paper they report the lead rider of two riders riding in single file receives a reduction in energy demand of 2.6% while riding in the time trial position.

Above are three examples of data from a similar situation, with reported reductions in energy (power) demand to ride at the same speed ranging between 2% to 3% for the lead rider compared with riding solo.

There's another paper that reports a 5% advantage for the lead rider of team time trial, although I'm not able to see more than the abstract:

Aerodynamics of a cycling team in a time trial: does the cyclist at the front benefit?; European Journal of Physics, Volume 30 Number 6, 2009; A Íñiguez-de-la Torre and J Íñiguez

Edit: I've now read the paper and it used two dimensional CFD analysis on ellipses as a simple model simulation of multiple riders in a line and is indicative of the principles involved.

I've had the resources to test this for some time but I've hadn't got around to doing the experiment, mainly because exclusive use of track time costs money and I'm focussed on working with clients on answering more important aerodynamics questions for them than doing experiments just for the fun of it.

But today I had the opportunity to do just such an experiment.

I was doing aerodynamics testing as part of a story being written about a woman masters rider preparing for the UCI World Masters track cycling championships being held in Manchester later this year. Cycling NSW kindly offered and arranged for the track time to make this possible, and a client of mine, Rod Wagner, loaned a special power meter to enable the testing on the rider's track bike, while I offered my time for the aero work.

We'd reached the end of our allotted track time, but as luck would have it no one else was ready to ride on the track, so we had some spare time for the experiment, and willing participants.

I won't comment on the primary aero testing session as that's for another to write about for later publication in magazine and online, but I'll expand on the impromptu experiment.

The method of measurement

With the Alphamantis Track Aero System, I record and monitor in real time a rider's aerodynamics as they circulate around the indoor velodrome. Testing is done indoors as this removes the wind variable and provides for a well controlled environment. The system enables us to monitor speed and velocity and along with other key inputs such as air density, track geometry data, centre of mass height, rider mass and rolling resistance variables, the Coefficient of drag x Frontal area (CdA) is also plotted in real time and lap by lap a picture of a rider's aerodynamics is revealed.

I've briefly explained this system before in this post, which also has a video demo. You can also read more on the Alphamantis site linked above.

The experiment

Normally this testing is done with a rider riding solo on the track but for this experiment I asked her coach, another world level master's rider, to join in. His task was to ride in various positions relative to the test rider (who would continuously circulate around the track at approximately 40km/h) while her coach would change his relative position on the track every 4-6 laps as follows and on my instruction, he would:

- ride in front of the test rider to test the level of drafting assistance, then
- ride next to, and on the outside of the test rider, then
- ride immediately behind the test rider, then
- drop off entirely and stop riding, so that we could obtain data from the test rider circulating solo.

This test process was repeated a second time during the long test run to validate the results from the first run.

For reference, the test rider is a slim 60kg female approximately 172cm tall, and the coach weighs approximately 80kg and is ~185cm tall. The test rider was using a track bike with pursuit bars, while the other rider was using a track bike in regular mass start set up.

The system is really reporting the impact on apparent CdA. It's a quick way to measure how beneficial or detrimental having the other rider near you is, and the measurements are not overly sensitive to the changes in speed during the run (this is the nice thing about the process).

The results

Here's a table summarising the results of all the data runs. Click on images to see larger versions.

In the case of the support rider riding behind the test rider, the test rider gained a benefit of a reduction in apparent CdA of around 0.008m^2, or about 3.8%. Note (i) the error range and (ii) the support rider was riding in a more upright mass start position (and consequently has a larger "bow wave") and is somewhat larger than the test rider.

Also shown are the results of the traditional drafting, being a reduction in apparent CdA to nearly half of the solo value, and interestingly, the apparent CdA increase of ~ 0.013m^2, or nearly 6% when the other rider was riding alongside the test rider.

Since apparent CdA differences are a little harder to understand, I've flipped the data around to show, at a normalised velocity of 40km/h, what the power demand for the solo rider would be for each scenario:

The table below summarises the chart data, and also shows the difference in power compared with riding solo:

Compared with riding solo, the test rider gains a ~7W (3%) benefit from having her ride buddy directly behind her; a 76W (39%) benefit from drafting behind her ride buddy; and a 10W (5%)  penalty when her ride buddy is riding alongside.

So in this experiment, I found a 3% energy demand benefit from having a trailing rider, and that's right in line with (but at the top of) the range found by the other reported data referenced earlier.

This result of a 10W penalty when riding alongside another rider is more novel, although it doesn't surprise me it may be news to some.

It is something to ponder when riding in team formation events, especially when the lead rider pulls aside to make their way to the back of the line of riders. They and their team are better off (at least in low yaw conditions) if the rider pulls over and moves well away from their companions until they are near the back and can return to be in the draft of the other riders. 10W is nothing to sneeze at.


So it would seem that if you wish to ride alongside your ride buddy, it might cost you ~10W give or take. If speed is of the essence, then ride in single file, you'll both go quicker that way.

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Sunday, July 26, 2015

Alpe d'Huez: TDF Fastest Ascent Times 1982-2015

Update of the Alpe d'Huez climbing times and speed chart previously posted here and here. Read those previous posts for discussion of context.

Edit (28 July 2015): since posting this two days ago, I was alerted to some updates made to the 1991 ascent times. Two sources did work with archive video to better verify these times, the net result being an addition of 41 seconds to each of the 1991 ascent times.

Thanks to https://twitter.com/ammattipyoraily for the posting the data.

This chart shows the average speed of the five fastest ascents up the Alpe d'Huez climb for each year the Tour de France included this climb, with the exception being the times from the 1980s which are the average speeds for fewer riders (as data on five fastest ascents in those years is not available to me).

As a reminder, I chose to average the 5 fastest ascent times for a couple of reasons:
- it reduces the individual noise in the data for year by year comparisons
- the 5 fastest were most likely to have been giving it close to maximal effort and would be representative of the quality at pointy end of the field
- the available historical data I have on ascent times doesn't permit increasing that sample size all that much in any case.

 Here's the data in table format, along with some extra context information. I've also ranked the average ascent speeds of the 5 fastest for each of the 13 occasions during 1991-2015 that Alpe d'Huez was climbed. I left out ranking 1980s ascents as I don't have times for all 5 fastest riders for those years (IOW the actual average speed of 5 fastest would be lower).

As we can see, 2015 ranks as the 8th fastest TdF ascent over that period, when based on the 5 fastest ascents each year.

Here's the same table but with weather conditions for the airport nearest to Boug d'Oisans listed from 3pm to 5pm on the day of the race. I was only able to source data back to 1997. If anyone knows of an online almanac of weather data for near Bourg d'Oisans for years prior to 1996, please let me know.

Weather data source: http://www.wunderground.com/
Note the variability in temperature from year to year, and importantly the prevailing wind direction and speed. 

Now how such prevailing wind actually plays out on the slopes of the Alpe is hard to say, but we should expect some differences from year to year in the speed riders can attain given their power on the day.

Or put another way, any power estimates from ascension rates comparing year to year will have some error depending on how the localised wind plays out. The climb obvious has many changes of direction, and wind at rider level is different to the prevailing conditions (normally measured at 10m above ground level and as a rough estimate it's about half that at rider level). Of course localised wind will be shaped by the Alpe itself as well as boundary layer features such as trees, road cuttings, vehicles and so on.
Map: http://www.alpedhueznet.com/

The prevailing wind was from the North East in 1997, 1999, 2008, 2011 and 2015; from the North West in 2003 and 2013; from the South West in 2001 and 2006 and from the West in 2004.

Course profile shows the climb is not a constant gradient:
Source: http://bike-oisans.com/wp-content/uploads/2013/02/profil-montee-alpe-d-huez.png

Fastest five ascents up Alpe d'Huez from this year's stage were:

and here are the fastest 5 riders by year (click to see larger version), with lines marking the time of the 50th and 100th fastest ascents of all time:

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Friday, July 17, 2015

Climbing power estimates: Windbags II

No specific comment, I just wanted to create a public link to the following 2014 study investigating the accuracy of climbing power estimates and to include a graphic and quote the study's conclusion.

My earlier comments on this topic of estimation accuracy can be found in this post from two years ago:

The study is:
Accuracy of Indirect Estimation of Power Output From Uphill Performance in Cycling 
Grégoire P. Millet, Cyrille Tronche, and Frédéric Grappe
International Journal of Sports Physiology and Performance, 2014, 9, 777-782 http://dx.doi.org/10.1123/IJSPP.2013-0320 © 2014 Human Kinetics, Inc.


Study Conclusions:

Aerodynamic drag (affected by wind velocity and orientation, frontal area, drafting, and speed) is the most confounding factor. The mean estimated values are close to the power-output values measured by power meters, but the random error is between ±6% and ±10%. Moreover, at the power outputs (>400 W) produced by professional riders, this error is likely to be higher. This observation calls into question the validity of releasing individual values without reporting the range of random errors.

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Friday, July 10, 2015

Aero for slower riders. Part II

A couple of years ago in this blog item I explained how there really aren't riders too slow to gain speed benefit from an aerodynamic improvement. I demonstrated how the same aero benefit actually resulted in greater time savings for slower riders over a fixed distance course.

That might seem counter intuitive to begin with, but it's simply because the relative speed gains are almost the same for everyone, and that the slower riders are on course for longer, thereby shaving more time from their ride.

Of course as I mentioned in my previous item the development priorities for every rider will be different, and whether or not spending time, effort, money or other resources on improving aerodynamics is a priority depends very much on your objectives and what your other development priorities are. Keep in mind it is possible to work on various aspects of performance simultaneously, it's not an either/or proposition.

That said, this is really just to cover the physics, which shows us that it really doesn't matter what level of rider you are, there is a speed benefit to improving aerodynamics, and the benefit is pretty much the same for everyone.

So here's the chart*:

It shows three sets of data. The lines plot the speed an rider would sustain on flat road at various power outputs from 100 watts to 400 watts. Put out more power, you go faster. That's pretty obvious.

I plot two of those lines, one each for a given coefficient of drag area (CdA) of 0.32m^2 and one for a CdA of 0.30m^2. Note that these CdA values are approximately midway between values typical for a rider of the size modelled on a road bike and position and a time trial bike and position.

A 0.02m^2 (6.25%) reduction in CdA is entirely possible with clothing, helmet and wheel choices. Of course it's also possible to attain such a drop from positional changes.

How much any individual can reduce their CdA depends on many factors, mostly how (un)aerodynamic they are to begin with. Some people have a greater opportunity for improvement than others.

In any case, the line with the same lower CdA shows a higher speed for each of the power outputs which is to be expected.

Below those lines I show with the red columns the proportional increase in speed attained from that 6.25% reduction in CdA. It ranges from 1.96% increase in speed at 100W to 2.09% increase in speed at 400W.

So while a faster/more powerful rider gains more speed from the same drop in CdA, the relative speed gains are pretty much the same at around 2% across a wide spectrum of power outputs.

OK, as I said last time, putting on some flash aero wheels and a skinsuit won't turn a local club amateur into a pro bike rider, but suggesting that a rider is too slow to gain speed from an aerodynamic improvement is nonsense.

And what's interesting is that all riders, be they fast or slow, benefit almost equally from the same aerodynamic improvement.

* And once again the data is derived using the same model as described in this paper:

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Tuesday, June 09, 2015

W/m^2, Altitude and the Hour Record. Part III

In my previous posts on this topic I explored the impact of altitude on the hour record. You can recap by clicking on the links here:

W/m^2, Altitude and the Hour Record. Part I
W/m^2, Altitude and the Hour Record. Part II

In summary, the primary impacts on the speed attainable (or distance attainable for an hour) are:

1. Physiological - the reduction in sustainable aerobic power as altitude increases due to the reduced partial pressure of Oxygen, and

2. Physical - the reduction in aerodynamic drag as altitude increases due to the reduction air density.

Of course there are other factors - variable track surfaces and geometry, logistical, financial, physiological and so on, but for the purpose of this exercise I have confined analysis to the primary physiological and physical impacts.

These primary competing factors - reduced power and reduced drag combine to mean that in general an increase in altitude means a greater speed is attainable. In other words, the benefit of the lower air resistance at higher altitude typically outweighs the reduction in power. But not always.

The level of impact to speed is individual and is a function of each individual's physiological response to altitude - while the physics side of the equation is the same for everybody. I covered this in more detail in Part II of this series, and used data from several studies which provide four formula for the average impact of altitude on power output.

I plotted the different formula depending on whether athletes had acclimatised to altitude or not.

This chart should be fairly intuitive - further up in altitude you go, the more power you lose compared with sea level performance. The vertical scale of the chart amplifies the differences between them, which are not large, but also not insignificant either. A key element was the difference between athletes that had acclimated to altitude and those who had not.

Then I layered on that the physics impact of reducing air resistance, but the resulting chart was not quite as intuitive to follow and so I decided to revisit this another way.

Hence exhibit A below (click on the image to view larger version):

This should be reasonably straightforward to interpret, but even so I'll  provide some explanation.

The horizontal axis is altitude and the dark vertical lines represent the altitude of various tracks around the world.

The vertical axis is the proportion of sea level speed attainable.

The curved coloured lines represent the combined impact of both a reduction in power using each of the formula discussed in Part II of this series, combined with the reduction in air resistance.

So for example, if we look at the green line (Basset et al acclimated), this shows that as an cyclist increases altitude, they are capable of attaining a higher speed up until around 2,900 metres, and any further increase in altitude shows a decline in the speed attainable, as the power losses begin to outweigh the reduction in air density.

The track in Aigle Switerland represents around a 1% speed gain over London, while riding at Aguascalientes would provide for between a 2.5% to 4% gain in speed. Head to Mexico City and you might gain a little more, but as the chart shows, the curves begin to flatten out, and so the risk v reward balance tips more towards the riskier end of the spectrum.

Altitude therefore represents a case of good gains but diminishing returns as the air gets rarer. Once you head above 2,000 metres, the speed gains begin to taper off, and eventually they start to reduce, meaning there is a "sweet spot" altitude.

Caveats, and there are a few but the most important are:
-  any individual's sweet spot altitude will depend on their individual response to altitude - the plotted lines represent averages for the athletic groups studied;
- the formula used have a limited domain of validity, while the plotted lines extend beyond that, a point I also covered in Part II of this series;
- these are not the only performance factors to consider, but are two of the most important.

I suspect that the drop off in performance with altitude might occur a little more sharply for many than is suggested here. Nevertheless, the same principles apply even if your personal response to altitude is on the lower end of the range, and it is hard to imagine why anyone would suggest that heading to at least a moderate altitude track is a bad idea from a performance perspective.

Alex Dowsett rode 52.937km at Manchester earlier this year. At Aguascalientes he could reasonably expect to gain ~3.5% +/-0.5%  more speed, or just about precisely what Bradley Wiggins attained in London.

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Monday, June 08, 2015

Density matters

I saw a question today from someone who read recent comments about how high air pressure resulted in Brad Wiggins' hour distance being less than it might otherwise have been with more favourable conditions.

He was wondering if you can control air pressure in velodromes, or choose a time of year when it is lower. So can we do that?

Climate control

While there are velodromes where the inside air temperature is controllable (mostly northern hemisphere tracks located in cold climates), the control of air pressure is not something possible at any currently existing track that I'm aware of.

It would require quite a deal of engineering, in particular to provide an air lock / sealed environment that enables lots of people (and service vehicles) to enter /exit the building without affecting inside pressures and which meets emergency evacuation requirements for a large crowd, as well as fresh air to breathe. I don't see that happening any time soon.

Air locks do exist, e.g. at Aguascalientes velodrome in Mexico they use an air pressure differential to support the roof, but that means the air pressure inside the velodrome needs to be higher than outside. Not by much, but it will always need to be higher relative to local weather conditions, and inside the velodrome air pressure will still vary relative to outdoors.

So what about picking a better time of year?

Well let's look at the daily barometric pressure readings near London for the past three and a half years. Source for these charts is the National Physical Laboratory in the UK.

Barometric pressure London Jan-Dec 2012

Barometric pressure London Jan-Dec 2013

Barometric pressure London Jan-Dec 2014

Barometric pressure London Jan-June to date 2015

Looking at the above, it's pretty clear there is no obvious pattern to suggest a time of year when barometric pressure will be, on the balance of probabilities, lower.

Air density is what matters.

Air pressure of course is not the only variable. What really matters is attaining as low an air density as is physiological sensible. Air density along with a rider's aerodynamics, ie. their CdA, determines the energy demand for riding at a given speed, and lower air density is desirable for greater speed, provided of course the means to achieve that lower density doesn't reduce a rider's power to the extent performance ends up being worse. e.g. by riding at such high altitudes or temperatures that the rider's power output is compromised to a greater extent than the air density benefit provides.

Air density is a function of:
- air temperature
- barometric pressure
- altitude
- relative humidity

You can pretty much discount the latter as the changes in air density is very small with changes in humidity, although for the record humid air is slightly less dense than dry air (at same temperature, pressure and altitude).

Air density reduces with increasing temperature and altitude, and with reducing barometric pressure.

Since attempting to reduce air pressure either via climate control or by picking suitable times of year is not really an option, that leaves us with adjusting the other two variables - temperature and altitude.

I've discussed altitude before in this item. I'm going to revisit it in a future post in an attempt to simplify the impact of the variables involved.

Heating the air inside a velodrome is common, and this was attempted with some powerful portable heating devices during Jack Bobridge's unsuccessful attempt earlier this year, and in the case of attempts at most northern hemisphere tracks, the temperature has been dialled up to the rider's desired level.

Wiggins did specific heat acclimation work and reports are the temperature inside the velodrome was around 28-30C. That's pretty warm - going too hot can be detrimental as power losses can occur with inadequate cooling. As I said earlier, it's a balance between a physical benefit and a potential physiological cost.

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Wiggo's Hour

Just a short one today to update the chart from the one I posted here and on other social media forums. Click to see bigger version.


Different reports of barometric pressure of 1031-1036hPa and air temp of 30.3C inside the track mean that Wiggins must have been exceptionally aerodynamic and recent work on his bike and position at the track suggest some good aero gains were made.

I estimate a power to CdA ratio of 2500-2550W/m^2 was required.

There are of course a range of assumptions:
Total mass: 82kg
Crr: 0.0023
Drivetrain efficiency: 98%
Altitude: 50m
Relative Humidity: 60%

If drivetrain efficiency is better, say 99% and Crr at 0.0020, then it drops the power to CdA ratio down to 2200-2220W/m^2.

and perfect pacing.

Just on that, my colleague Xavier Disley has once again produced a lap pacing chart - here it is:

That's a very slight fade over the course of an hour, which in my humble opinion is pretty much perfect. Opening few laps a bit hard, but that's understandable as a rider seeks to control the adrenaline rush with thousands in the crowd watching on and cheering.

The high air pressure did cost distance, and on another day perhaps 55km was within reach

As for going to high altitude, well there are many variables, but another 1-2km is feasible. See this item for more on that.

Well done to Brad Wiggins. That's sure a fine ride.

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